mlsanityData to 7 Oct 2026

Trust & Safety of LLMs and LLMs in Trust & Safety

arXiv 2412.02113 ↗First posted 3 Dec 2024cs.AIcited by 3

On the map: LLM safety and security → Safety alignment, refusal and guardrails

Abstract (arXiv)

In recent years, Large Language Models (LLMs) have garnered considerable attention for their remarkable abilities in natural language processing tasks. However, their widespread adoption has raised concerns pertaining to trust and safety. This systematic review investigates the current research landscape on trust and safety in LLMs, with a particular focus on the novel application of LLMs within the field of Trust and Safety itself. We delve into the complexities of utilizing LLMs in domains where maintaining trust and safety is paramount, offering a consolidated perspective on this emerging trend. By synthesizing findings from various studies, we identify key challenges and potential solutions, aiming to benefit researchers and practitioners seeking to understand the nuanced interplay between LLMs and Trust and Safety. This review provides insights on best practices for using LLMs in Trust and Safety, and explores emerging risks such as prompt injection and jailbreak attacks. Ultimately, this study contributes to a deeper understanding of how LLMs can be effectively and responsibly utilized to enhance trust and safety in the digital realm.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Safety alignment, refusal and guardrails, one of 12 directions in LLM safety and security. Builds on 16 of its 27 references that are in the map. First posted on arXiv 3 Dec 2024.

Builds on

16 of 27 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024SafetyPrompts: a Systematic Review of Open Datasets for Evaluating and Improving Large Language Model Safety8 Apr

Other references · 15

  1. 2021TruthfulQA: Measuring How Models Mimic Human Falsehoodscited by 2,919
  2. 2020Extracting Training Data from Large Language Modelscited by 2,144
  3. 2022Red Teaming Language Models with Language Modelscited by 983
  4. 2021Ethical and social risks of harm from Language Modelscited by 957
  5. 2023Large Language Modelscited by 774
  6. 2023Jailbreaking ChatGPT via Prompt Engineering: An Empirical Studycited by 478
  7. 2023SemDeDup: Data-efficient learning at web-scale through semantic deduplicationcited by 295
  8. 2024TrustLLM: Trustworthiness in Large Language Modelscited by 280
  9. 2023Poisoning Web-Scale Training Datasets is Practicalcited by 276
  10. 2023A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validationcited by 91
  11. 2024Can Large Language Models Beat Wall Street? Unveiling the Potential of AI in Stock Selectioncited by 29
  12. 2024Large Language Models and User Trust: Consequence of Self-Referential Learning Loop and the Deskilling of Healthcare Professionalscited by 13
3 more
  1. 2024On Efficient and Statistical Quality Estimation for Data Annotationcited by 8
  2. 2024Large Language Models are Skeptics: False Negative Problem of Input-conflicting Hallucinationcited by 4
  3. 2024Evaluating Deduplication Techniques for Economic Research Paper Titles with a Focus on Semantic Similarity using NLP and LLMscited by 1

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Cited by year

Citing papers per year: 2025: 3.

3 papers.

  1. 2025Decoding Uncertainty: The Impact of Decoding Strategies for Uncertainty Estimation in Large Language Modelscited by 3
  2. 2025CASE: An Agentic AI Framework for Enhancing Scam Intelligence in Digital Paymentscited by 1
  3. 2025Red Teaming for Generative AI, Report on a Copyright-Focused Exercise Completed in an Academic Medical Centercited by 0